Related Experiment Video
Updated: May 12, 2026
07:13
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
Published on: May 27, 2020
Resting brains never rest: computational insights into potential cognitive architectures
Gustavo Deco1, Viktor K Jirsa, Anthony R McIntosh
1Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain. gustavo.deco@upf.edu
Trends in Neurosciences
|April 9, 2013
Summary
Resting-state networks (RSNs) are best simulated by large-scale cortical models at the edge of instability. This dynamic state allows for efficient network mobilization and reflects the brain's capabilities, emphasizing time and space interplay.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Theoretical Neuroscience
Background:
- Resting-state networks (RSNs) are a key focus in neuroimaging.
- Large-scale cortical models suggest RSNs operate near instability.
- This near-critical state is hypothesized to optimize brain function.
Purpose of the Study:
- To propose a new theoretical framework for understanding resting-state networks.
- To provide a foundation for empirical testing of RSN dynamics.
- To elucidate the role of network instability in brain function.
Main Methods:
- Simulation of large-scale cortical models.
- Analysis of network dynamics near the edge of instability.
- Theoretical modeling of functional network configurations.
Main Results:
- Networks at the edge of instability exhibit a low firing stable state.
- Extrinsic perturbations shape task-related dynamics.
- Intrinsic noise generates excursions reflecting available functional networks.
- This state enhances efficiency and speed of network mobilization.
Conclusions:
- The resting state reflects the brain's dynamical capabilities.
- The interplay of time and space is crucial for RSNs.
- The proposed framework offers a testable model for RSN research.
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